These are some of the key terms and aims from Data Feminism. I am mostly drawing from the introduction.

Please add more in the comments! It is always best to write and read in collaboration, so you all have some things valuable contributions to make here that I can’t make on my own.

1. Oppression. In the introduction, the paragraph that begins “Key to the idea of intersectionality,” D’Ignazio and Klein define oppression as “the systematic mistreatment of certain groups of people by other groups.” Important in this definition is the term “systematic.” Collins’ matrix of domination is useful for thinking about how oppression can occur in ways most people are familiar with (e.g., someone calling someone else a racial or sexist slur), but it often happens most impactfully in larger ways through:
   • policies and laws (e.g., the practice of redlining, which made home ownership nearly impossible for many African Americans)
   • the enforcement of policies and laws (e.g., trends of how adoption agencies might approve of applications from LGBTQ+ couples)
   • cultural messages that are received (e.g., dialogue between women in films as evidence of their lack of centrality as characters in films).
2. Privilege. Privilege can be thought of as unearned advantages some groups of people have over others. This is not the same thing as saying that someone did not work hard, or that someone does not deserve something. It just means that someone is benefiting from having opportunities available to them to work hard, dazzle with their brilliance, or flourish in whatever way they are able to (or, in some cases, they might just be mediocre and lucky…but still afforded an opportunity to flourish). One way to put it is that some groups are more likely to have good choices–in other words, some people don’t make good choices as much as have those choices available to them to make in the first place. As D’Ignazio and Klein note, one especially complex scenario is the “privilege hazard”–it can be hard to see oppression when you benefit from it.
3. Power. In chapter 1, D’Ignazio and Klein define power as “the current configuration of structural privilege and structural oppression, in which some groups experience unearned advantages–because various systems have been designed by people like them and work for people like them–and other groups experience systematic disadvantages–because those same systems were not designed by them or with people like them in mind.”
4. Feminism. In the introduction, starting with the sentence “As should already be apparent,” D’Ignazio and Klein talk through definitions of feminism. They define it for their purposes fairly broadly as “the diverse and wide-ranging projects that name and challenge sexism and other  forces of oppression, as well as those which seek to create more just, equitable, and livable futures.”
5. Intersectionality. In the paragraph starting “Key to the idea of intersectionality,” D’Ignazio and Klein define intersectionality as not just “intersecting aspects of any particular identity,” but also the “intersecting forces of privilege and oppression at work in a given society.” So, as we saw in chapter 1, Serena Williams deals with consequences of oppression as a person who is Black and as a woman, but she also has access to privileges afforded to the wealthy and to celebrities. It is important to not see things as “added on” but as combining in ways that are unique: Williams is oppressed as a Black woman rather than being Black + woman.
6. Rhetoric. Rhetoric has many, many definitions. For our purposes, we can define it as the intentional use of symbols that is used to influence a range of organisms and objects, often with an aim toward persuasion (i.e., to make something believe and/or do something.
7. Data. Information that is argued and shaped into something claimed as credible and/or important. Data comes into existence by making an ethical appeal about information (information is also rhetorical, but has less of connotation of credibility compared to “data”). This is not the same thing as saying that all data are untrue, only that someone must do something with symbols to create data. Truth is a different conversation to have than whether data is rhetorical or not.

Seven Principles. In the introduction, the seven principles D’Ignazio and Klein cite are very instructive for the aims of the book and how you might think about how to guide your own projects this semester.